Image processing method and device

ABSTRACT

An image processing method and device is provided. Components of a point Ai(ri,gi,bi) of an original image in RGB color space are processed according to a first function f1(x) to obtain A0(r0,g0,b0)=f1(xi). The point Ai(ri,gi,bi) of the original image is processed according to a second function f2(x) to obtain a processed saturation S0=f2(xi). Let f2(xi)=1 to determine the point A0(r0,g0,b0). The point Ai is processed in the RGB color space if max(r0,g0,b0)≤1. The point Ai is converted into CMY color space for image processing if max(r0,g0,b0)&gt;1.

BACKGROUND 1. Field of Disclosure

The present disclosure relates to image processing technologies, and more particularly to an image processing method and device.

2. Description of Related Art

With improvements on living standards, demands on display quality of electronic products are increasingly high. In existing skills, in order to improve the display quality, image processing is performed during screen display. During the image processing, it is usually necessary to adjust saturation of images in order to make the display more colorful.

For color image saturation enhancement, it is very important to ensure no out-of-boundary issue for RGB color space and keep color tone unchanged. Color image enhancement is usually performed in HSI (Hue, Saturation, Intensity) and HSV (Hue, Saturation, Value) spaces through conversion. However, a color space transformation problem will occur in converting processed images back to the RGB space. Generally, cut-out approaches are adopted to map the out-of-boundary values to boundary values. This may cause some details to be lost and cause color tone to be changed. Further, this space transformation approach takes time, and is large in computation consumption and low in efficiency.

FIG. 1 is a flowchart of a method for enhancing saturation of an RGB color image using space transformation. First, the color image that is to be processed is converted into HSI space, its saturation is extracted, and the saturation is enhanced using a certain approach to obtain an enhanced saturation value. The processed HSI model is converted back to RGB model space using a model transformation formula. After normalization in the RGB model space, each RGB component is within a range of 0 to 1. The RGB component obtained from HSI model conversion has a problem that its value may exceed 1. By using a general cut-out approach, color tone may be inconsistent.

SUMMARY

The present invention provides an image processing method for solving image color tone inconsistence caused by being out of the boundaries of color space.

To achieve above object, technical schemes provided in the present disclosure are described below.

The present disclosure provides an image processing method, including:

Step S10: according to a first function f₁(x), processing components of a point A_(i)(r_(i),g_(i),b_(i)) of an original image in RGB color space to obtain A₀(r₀,g₀,b₀)=f₁(x_(i)), where i is a natural number;

Step S20: according to a second function f₂(x), processing the point A_(i)(r_(i),g_(i),b_(i)) of the original image to obtain a processed saturation S₀=f₂(x_(i)); and

Step S30: letting f₂(x_(i))=1 to determine the point A₀(r₀,g₀,b₀), and processing the point A_(i) in the RGB color space if max(r₀,g₀,b₀) converting the point A_(i) into CMY color space for image processing if max(r₀,g₀,b₀)>1.

In accordance with a preferred embodiment of the present disclosure, Step S10 includes:

Step S11: selecting the point A_(i)(r_(i),g_(i),b_(i)) from the original image in the RGB color space; and

Step S12: according to the first function f₁(x), processing each component of the point A_(i) of the original image in the RGB color space to obtain A₀(r₀,g₀,b₀)=f₁(x_(i)),

wherein processing each component of the point A_(i) of the original image in the RGB color space is to stretch (α) and translate (β) the components of the point A_(i) in the RGB color space, where the first function is f₁(x)=αx+β.

In accordance with a preferred embodiment of the present disclosure, Step S20 includes:

Step S21: selecting the point A_(i)(r_(i),g_(i),b_(i)) from the original image in the RGB color space;

Step S22: according to a third function f₃(x), determining saturation S_(i)=f₃(x_(i)) of the point A_(i)(r_(i),g_(i),b_(i)) of the original image; and

Step S23: according to the second function f₂(x), processing the saturation S_(i) of the original image to obtain the processed saturation S₀=f₂(x_(i)).

In accordance with a preferred embodiment of the present disclosure, the saturation of the point A_(i)(r_(i),g_(i),b_(i)) of the original image is S_(i)=1−3×min[r_(i),g_(i),b_(i)]/r_(i)+g_(i)+b_(i); and

the processed saturation obtained by processing the saturation S_(i) of the original image according to the second function f₂(x) is S₀=f₂(x)=1−3×α·min[r_(i),g_(i),b_(i)]+β/r_(i)+g_(i)+b_(i).

In accordance with a preferred embodiment of the present disclosure, brightness of the original image remains unchanged before and after image processing, and values of α and β in f₁(x) are obtained using S₀=f₂(x_(i)) and A₀(r₀,g₀,b₀)=f₁(x_(i)).

In accordance with a preferred embodiment of the present disclosure, the CMY color space is a color model based on subtractive color mixture, and the point A_(i) is processed in the CMY color space using a fourth function f₄(x)=1−x, where x represents each component of the point A_(i)(r_(i),g_(i),b_(i)) in the RGB color space.

The present disclosure further provides an image processing device, including:

an image processing module configured to process components of a point A_(i)(r_(i),g_(i),b_(i)) of an original image in RGB color space according to a first function f₁(x) to obtain A₀(r₀,g₀,b₀)=f₁(x_(i)), where i is a natural number, process the point A_(i)(r_(i),g_(i),b_(i)) of the original image according to a second function f₂(x) to obtain a processed saturation S₀=f₂(x_(i)), determine the point A₀(r₀,g₀,b₀), process the point A_(i) in the RGB color space if max(r₀,g₀,b₀)≤1, and convert the point A_(i) into CMY color space for image processing if max(r₀,g₀,b₀)>1.

In accordance with a preferred embodiment of the present disclosure, the image processing module is configured to stretch (α) and translate (β) each component of the point A_(i) of the original image in the RGB color space, where the first function is f₁(x)=αx+β.

In accordance with a preferred embodiment of the present disclosure, the image processing module is configured to determine saturation S_(i)=f₃(x_(i)) of the point A_(i)(r_(i),g_(i),b_(i)) of the original image according to a third function f₃(x), where

${S_{i} = {1 - {3 \times \frac{\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}{r_{i} + g_{i} + b_{i}}}}};$

and

the image processing module is further configured to process the saturation S_(i) of the original image according to the second function f₂(x) to obtain the processed saturation

$S_{0} = {{f_{2}(x)} = {1 - {3 \times {\frac{{\alpha \cdot {\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}} + \beta}{r_{i} + g_{i} + b_{i}}.}}}}$

The present disclosure further provides an image processing method, including:

Step S10: according to a first function f₁(x), processing components of a point A_(i)(r_(i),g_(i),b_(i)) of an original image in RGB color space to obtain A₀(r₀,g₀,b₀)=f₁(x_(i)), where i is a natural number;

Step S20: according to a second function f₂(x), processing the point A_(i)(r_(i),g_(i),b_(i)) of the original image to obtain a processed saturation S₀=f₂(x_(i)); and

Step S30: letting f₂(x_(i))=1 to determine the point A₀(r₀,g₀,b₀), and processing the point A_(i) in the RGB color space if max(r₀,g₀,b₀)≤1; using a fourth function f₄(x) to convert the point A_(i) into CMY color space for image processing if max(r₀,g₀,b₀)>1.

In accordance with a preferred embodiment of the present disclosure, Step S10 includes:

Step S11: selecting the point A_(i)(r_(i),g_(i),b_(i)) from the original image in the RGB color space; and

Step S12: according to the first function f₁(x), processing each component of the point A_(i) of the original image in the RGB color space to obtain A₀(r₀,g₀,b₀)=f₁(x_(i)),

wherein processing each component of the point A_(i) of the original image in the RGB color space is to stretch (α) and translate (β) the components of the point A_(i) in the RGB color space, where the first function is f₁(x)=αx+β.

In accordance with a preferred embodiment of the present disclosure, Step S20 includes:

Step S21: selecting the point A_(i)(r_(i),g_(i),b_(i)) from the original image in the RGB color space;

Step S22: according to a third function f₃(x), determining saturation S_(i)=f₃(x_(i)) of the point A_(i)(r_(i),g_(i),b_(i)) of the original image; and

Step S23: according to the second function f₂(x), processing the saturation S_(i) of the original image to obtain the processed saturation S₀=f₂(x_(i)).

In accordance with a preferred embodiment of the present disclosure, the saturation of the point A_(i)(r_(i),g_(i),b_(i)) of the original image is

${S_{i} = {1 - {3 \times \frac{\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}{r_{i} + g_{i} + b_{i}}}}};$

and

the processed saturation obtained by processing the saturation S_(i) of the original image according to the second function f₂(x) is

$S_{0} = {{f_{2}(x)} = {1 - {3 \times {\frac{{\alpha \cdot {\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}} + \beta}{r_{i} + g_{i} + b_{i}}.}}}}$

In accordance with a preferred embodiment of the present disclosure, brightness of the original image remains unchanged before and after image processing, and values of α and β in f₁(x) are obtained using S₀=f₂(x_(i)) and A₀(r₀,g₀,b₀)=f₁(x_(i)).

In accordance with a preferred embodiment of the present disclosure, the CMY color space is a color model based on subtractive color mixture, and the point A_(i) is processed in the CMY color space using a fourth function f₄(x)=1−x, where x represents each component of the point A_(i)(r_(i),g_(i),b_(i)) in the RGB color space.

Beneficial effects of the present disclosure are described below. The present disclosure provides an image processing method and device. Points of an original image in its color space are filtered. By performing space transformation for the points that may be out of boundary, the present disclosure can efficiently solve the image distortion issue caused by being out of the boundaries of the color space, ensure unchanged color tone, and improve display quality. Also, image saturation is enhanced in the RGB color space and computing power is improved.

BRIEF DESCRIPTION OF DRAWINGS

For explaining the technical schemes used in the conventional skills and the embodiments of the present disclosure more clearly, the drawings to be used in descripting the embodiments or the conventional skills will be briefly introduced in the following. Obviously, the drawings below are only some embodiments of the present disclosure, and those of ordinary skill in the art can further obtain other drawings according to these drawings without making any inventive effort.

FIG. 1 is a basic image processing flowchart in an existing skill.

FIG. 2 is a flowchart of an image processing method in accordance with a first embodiment of the present disclosure.

FIG. 3 illustrates a comparison between an existing skill and the present disclosure in color image saturation enhancing approaches in RGB color space in accordance with a first embodiment of the present disclosure.

FIG. 4 illustrates a comparison between an existing skill and the present disclosure in color image saturation enhancing approaches in RGB color space in accordance with a second embodiment of the present disclosure.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following descriptions for the respective embodiments are specific embodiments capable of being implemented for illustrating the present disclosure with referring to the appended figures. In describing the present disclosure, spatially relative terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “inner”, “outer”, “lateral”, and the like, may be used herein for ease of description as illustrated in the figures. Therefore, the spatially relative terms used herein are intended to illustrate the present disclosure for ease of understanding, but are not intended to limit the present disclosure. In the appending drawings, units with similar structures are indicated by the same reference numbers.

In existing image processing methods, color images are converted into HSI (Hue, Saturation, Intensity) and HSV (Hue, Saturation, Value) color spaces and are processed in these color spaces. When they are converted back to RGB color space, some details may be lost and color tone may be changed because of being out of the boundaries of the color space. The present disclosure provides an image processing method, and embodiments of the present disclosure can avoid these drawbacks.

FIG. 2 is a flowchart of an image processing method in accordance with a first preferred embodiment of the present disclosure. The method includes the following steps.

In Step S10, components of a point A_(i)(r_(i),g_(i),b_(i)) of an original image in RGB color space is processed according to a first function f₁(x) to obtain A₀(r₀,g₀,b₀)=f₁(x_(i)), where i is a natural number.

In the RGB color space, the point A_(i)(r_(i),g_(i),b_(i)) is selected from the original image. Each component of the point A_(i) of the original image is processed in the RGB color space according to the first function f₁(x) to obtain A₀(r₀,g₀,b₀)=f₁(x_(i)).

Processing each component of the point A_(i) of the original image in the RGB color space is to stretch (α) and translate (β) the components of the point A_(i) in the RGB color space.

In Step S20, the point A_(i)(r_(i),g_(i),b_(i)) of the original image is processed according to a second function f₂(x) to obtain a processed saturation S₀=f₂(x_(i)).

In the RGB color space, the point A_(i)(r_(i),g_(i),b_(i)) is selected from the original image. Saturation S_(i)=f₃(x_(i)) of the point A_(i)(r_(i),g_(i),b_(i)) of the original image is determined according to a third function f₃(x). The saturation S_(i) of the original image is processed according to the second function f₂(x) to obtain the processed saturation S₀=f₂(x_(i)).

The saturation is obtained according to S_(i)=1−3×min[r_(i),g_(i),b_(i)]/r_(i)+g_(i)+b_(i). The processed saturation is obtained according to

$S_{i} = {1 - {3 \times {\frac{\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}{r_{i} + g_{i} + b_{i}}.}}}$

$S_{0} = {{f_{2}(x)} = {1 - {3 \times {\frac{{\alpha \cdot {\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}} + \beta}{r_{i} + g_{i} + b_{i}}.}}}}$

Brightness of the original image remains unchanged before and after image processing, and values of α and β in f₁(x) are obtained using S₀=f₂(x_(i)) and A₀(r₀,g₀,b₀)=f₁(x_(i)).

In Step S30, let f₂(x_(i))=1 to determine the point A₀(r₀,g₀,b₀). The point A_(i) is processed in the RGB color space if max(r₀,g₀,b₀)≤1.

If max(r₀,g₀,b₀)>1, the point A_(i) is converted into CMY color space for image processing.

In the above formulas, let f₂(x_(i))=1 to determine values of r₀,g₀,b₀ for the point A₀(r₀,g₀,b₀).

According to a size of the obtained values of r₀,g₀,b₀, select a corresponding color space to process the point A_(i).

If max(r₀,g₀,b₀)≤1, the point A_(i) is processed in the RGB space using the second function f₂(x). If max(r₀,g₀,b₀)>1, the point A_(i) is converted into CMY color space using a fourth function f₄(x) and processed in the CMY color space.

The converted point is processed in the CMY color space. After being processed, the point is converted from the CMY color space to the RGB color space using the fourth function f₄(x) again.

The CMY color space is a color model based on subtractive color mixture. The fourth function is f₄(x)=1−x , where x represents each component of the point A_(i)(r_(i),g_(i),b_(i)) in the RGB color space.

For instance, a point A_(i)(r_(i),g_(i),b_(i)) is selected in the RGB color space, where i is a natural number. After being processed in the RGB color space, the point is noted as A₀(r₀,g₀,b₀).

(1) The first function f₁(x)=αx+β is used to stretch (α) and translate (β) the point A_(i)(r_(i),g_(i),b_(i)) in the RGB color space to obtain the following equations:

r ₀ =αr _(i)+β  (1-1)

g ₀ =αg _(i)+β  (1-2)

b ₀ =αb _(i)+β  (1-3)

(2) Brightness of the original image remains unchanged before and after image processing. Based on this principle, the following equations are obtained:

l _(i) =r _(i) +g _(i) +b _(i)   (1-4)

l ₀ =r ₀ +g ₀ +b ₀   (1-5)

According to equations (1-1)˜(1-5), a relation between α and β is obtained:

r _(i) +g _(i) +b _(i)=α(r _(i) +g _(i) +b _(i))+β  (1-6)

(3) Saturation of the point A_(i)(r_(i),g_(i),b_(i)) of the original image is determined according to the third function f₃(x). The following equation is obtained:

$\begin{matrix} {S_{i} = {1 - {3 \times \frac{\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}{r_{i} + g_{i} + b_{i}}}}} & \left( {1\text{-}7} \right) \end{matrix}$

(4) The saturation S_(i) of the original image is processed according to the second function f₂(x) to obtain a processed saturation S₀ as below:

$\begin{matrix} {S_{0} = {{f_{2}(x)} = {1 - {3 \times \frac{{\alpha \cdot {\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}} + \beta}{r_{i} + g_{i} + b_{i}}}}}} & \left( {1\text{-}8} \right) \end{matrix}$

Accordingly, values of α and β can be obtained according to equations (1-6) and (1-8).

In equation (1-8), let f₂(x_(i))=1 to determine values of r₀,g₀,b₀ for the point A₀(r₀,g₀,b₀). According to a size of the obtained values of r₀,g₀,b₀, select a corresponding color space to process the point A_(i).

If max(r₀,g₀,b₀)≤1, the point A_(i) is processed in the RGB space using the second function f₂(x). If max(r₀,g₀,b₀)>1, the point A_(i) is converted into CMY color space using a fourth function f₄(x) and processed in the CMY color space.

(5) According to the fourth function f₄(x), the point A_(i)(r_(i),g_(i),b_(i)) is processed in the CMY color space to obtain a point A_(i)(c_(i),m_(i),y_(i)) in the CMY color space as below:

c _(i)=1−r _(i)   (1-9)

m _(i)=1−g _(i)   (1-10)

y _(i)=1−b _(i)   (1-11)

(6) The first function f₁(x)=αx+β is used to stretch (α) and translate (β) the point A_(i)(c_(i),m_(i),y_(i)) in the CMY color space to obtain a processed point A₀(c₀,m₀,y₀) as below:

c ₀ =αc _(i)+β  (1-12)

m ₀ =αm _(i)+β  (1-13)

y ₀ =αy _(i)+β  (1-14)

(7) The saturation S_(i) of the original image is processed according to the second function f₂(x) to obtain a processed saturation S₀ as below:

$\begin{matrix} {S_{0} = {{f_{2}(x)} = {1 - {3 \times \frac{{\alpha \cdot {\min \left\lbrack {c_{i},m_{i},y_{i}} \right\rbrack}} + \beta}{c_{i} + m_{i} + y_{i}}}}}} & \left( {1\text{-}15} \right) \end{matrix}$

(8) The point A₀(c₀,m₀,y₀) is converted from the CMY color space into the RGB color space using the fourth function f₄(x), as below:

r ₀=1−c ₀   (1-16)

g ₀=1−m ₀   (1-17)

b ₀=1−y ₀   (1-18)

Accordingly, the afore-described method can filter points of the original image and perform space transformation for the points that may be out of boundary to solve the out-of-boundary issue.

FIG. 3 is a schematic diagram illustrating a cross section of RGB color space from brightness (0, 0, 0) to (1, 1, 1), where S is saturation and f(S) is a saturation enhancing function f₂(x).

FIG. 3A corresponds to a traditional saturation enhancing method. In case 1, r_(o),g_(o),b_(o) are within 0 to 1 after the saturation is enhanced. In case 2, after the saturation is enhanced, a maximum of r_(o),g_(o),b_(o) may exceed the range of 0 to 1, i.e., exceeding the boundary of the color space. Generally, cut-out approaches are adopted to solve the out-of-boundary issue. However, this may cause a color tone change before and after the image processing.

FIG. 3B corresponds to an improvement provided in this patent application. As can be seen from this figure, the points in case 2 are converted into the CMY color space. This can ensure the saturation to be within the range of 0 to 1 and will not cause the out-of-boundary issue.

The present disclosure further provides an image processing device. The device includes an image processing module.

Firstly, in the image processing module, a point A_(i)(r_(i),g_(i),b_(i)) is selected from the original image in the RGB color space. Each component of the point A_(i) of the original image is processed in the RGB color space according to the first function f₁(x)=αx+β to obtain a processed point A₀(r₀,g₀,b₀)=f₁(x_(i)).

The image processing is to stretch (α) and translate (β) each component of the point A_(i) of the original image in the RGB color space.

After that, in the image processing module, the point A_(i)(r_(i),g_(i),b_(i)) is selected from the original image in the RGB color space. Saturation S_(i)=f₃(x_(i)) of the point A_(i)(r_(i),g_(i),b_(i)) of the original image is determined according to the third function f₃(x). The saturation S_(i) of the original image is processed according to the second function f₂(x) to obtain a processed saturation S₀=f₂(x_(i)).

The saturation is obtained according to

$S_{i} = {1 - {3 \times {\frac{\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}{r_{i} + g_{i} + b_{i}}.}}}$

The processed saturation is obtained according to

$S_{0} = {{f_{2}(x)} = {1 - {3 \times {\frac{{\alpha \cdot {\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}} + \beta}{r_{i} + g_{i} + b_{i}}.}}}}$

Brightness of the original image remains unchanged before and after image processing, and values of α and β in f₁(x) are obtained using S₀=f₂(x_(i)) and A₀(r₀,g₀,b₀)=f₁(x_(i)).

Finally, in the image processing module, let f₂(x_(i))=1 to determine values r₀,g₀,b₀ of for the point A₀(r₀,g₀,b₀). According to a size of the obtained values of r₀,g₀,b₀, select a corresponding color space to process the point A_(i).

If max(r₀,g₀,b₀)≤1, the point A_(i) is processed in the RGB space using the second function f₂(x). If max(r₀,g₀,b₀)>1, the point A_(i) is converted into CMY color space using a fourth function f₄(x) and processed in the CMY color space.

The converted point is processed in the CMY color space. After being processed, the point is converted from the CMY color space to the RGB color space using the fourth function f₄(x) again.

The CMY color space is a color model based on subtractive color mixture. The fourth function is f₄(x)=1−x , where x represents each component of the point A_(i)(r_(i),g_(i),b_(i)) in the RGB color space.

For instance, a point A_(i)(r_(i),g_(i),b_(i)) is selected in the RGB color space, where i is a natural number. After being processed in the RGB color space, the point is noted as A₀(r₀,g₀,b₀).

(1) The first function f₁(x)=αx+β is used to stretch (α) and translate (β) the point A_(i)(r_(i),g_(i),b_(i)) in the RGB color space to obtain the following equations:

r ₀ =αr _(i)+β  (2-1)

g ₀ =αg _(i)+β  (2-2)

b ₀ =αb _(i)+β  (2-3)

(2) Brightness of the original image remains unchanged before and after image processing. Based on this principle, the following equations are obtained:

l _(i) =r _(i) +g _(i) +b _(i)   (2-4)

l ₀ =r ₀ +g ₀ +b ₀   (2-5)

According to equations (2-1)˜(2-5), a relation between α and β is obtained:

r _(i) +g _(i) +b _(i)=α(r _(i) +g _(i) +b _(i))+β  (2-6)

(3) Saturation of the point A_(i)(r_(i),g_(i),b_(i)) of the original image is determined according to the third function f₃(x). The following equation is obtained:

$\begin{matrix} {S_{i} = {1 - {3 \times \frac{\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}{r_{i} + g_{i} + b_{i}}}}} & \left( {2\text{-}7} \right) \end{matrix}$

(4) The saturation S_(i) of the original image is processed according to the second function f₂(x) to obtain a processed saturation S₀ as below:

$\begin{matrix} {S_{0} = {{f_{2}(x)} = {1 - {3 \times \frac{{\alpha \cdot {\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}} + \beta}{r_{i} + g_{i} + b_{i}}}}}} & \left( {2\text{-}8} \right) \end{matrix}$

Accordingly, values of α and β can be obtained according to equations (2-6) and (2-8).

In equation (2-8), let f₂(x_(i))=1 to determine values of r₀,g₀,b₀ for the point A₀(r₀,g₀,b₀). According to a size of the obtained values of r₀,g₀,b₀, select a corresponding color space to process the point A_(i).

If max(r₀,g₀,b₀)≤1, the point A_(i) is processed in the RGB space using the second function f₂(x). If max(r₀,g₀,b₀)>1, the point A_(i) is converted into CMY color space using a fourth function f₄(x) and processed in the CMY color space.

(5) According to the fourth function f₄(x), the point A_(i)(r_(i),g_(i),b_(i)) is processed in the CMY color space to obtain a point A_(i)(c_(i),m_(i),y_(i)) in the CMY color space as below:

c _(i)=1−r _(i)   (2-9)

m _(i)=1−g _(i)   (2-10)

y _(i)=1−b _(i)   (2-11)

(6) The first function f₁(x)=αx+β is used to stretch (α) and translate (β) the point A_(i)(c_(i),m_(i),y_(i)) in the CMY color space to obtain a processed point A₀(c₀,m₀,y₀) as below:

c ₀ =αc _(i)+β  (2-12)

m ₀ =αm _(i)+β  (2-13)

y ₀ =αy _(i)+β  (2-14)

(7) The saturation S_(i) of the original image is processed according to the second function f₂(x) to obtain a processed saturation S₀ as below:

$\begin{matrix} {S_{0} = {{f_{2}(x)} = {1 - {3 \times \frac{{\alpha \cdot {\min \left\lbrack {c_{i},m_{i},y_{i}} \right\rbrack}} + \beta}{c_{i} + m_{i} + y_{i}}}}}} & \left( {2\text{-}15} \right) \end{matrix}$

(8) The point A₀(c₀,m₀,y₀) is converted from the CMY color space into the RGB color space using the fourth function f₄(x), as below:

r ₀=1−c ₀   (2-16)

g ₀=1−m ₀   (2-17)

b ₀=1−y ₀   (2-18)

Accordingly, the afore-described method can filter points of the original image and perform space transformation for the points that may be out of boundary to solve the out-of-boundary issue.

FIG. 4 is a schematic diagram illustrating a cross section of RGB color space from brightness (0, 0, 0) to (1, 1, 1), where S is saturation and f(S) is a saturation enhancing function f₂(x).

FIG. 4A corresponds to a traditional saturation enhancing method. In case 1, r_(o),g_(o),b_(o) are within 0 to 1 after the saturation is enhanced. In case 2, after the saturation is enhanced, a maximum of r_(o),g_(o),b_(o) may exceed the range of 0 to 1, i.e., exceeding the boundary of the color space. Generally, cut-out approaches are adopted to solve the out-of-boundary issue. However, this may cause a color tone change before and after the image processing.

FIG. 4B corresponds to an improvement provided in this patent application. As can be seen from this figure, the points in case 2 are converted into the CMY color space. This can ensure the saturation to be within the range of 0 to 1 and will not cause the out-of-boundary issue.

The present disclosure provides an image processing method and device. Each component of a certain point of an original image is processed in RGB color space. The points of the original image in the RGB color space are filtered according to the processed results. By performing space transformation for the points that may be out of the boundary and converting them from the RGB color space into CMY color space, the present disclosure can efficiently solve the image distortion issue caused by being out of the boundaries of the color space, ensure unchanged color tone, and improve display quality. Also, image saturation is enhanced in the RGB color space and computing power is improved.

Above all, while the preferred embodiments of the present disclosure have been illustrated and described in detail, various modifications and alterations can be made by persons skilled in this art. The embodiment of the present disclosure is therefore described in an illustrative but not restrictive sense. It is intended that the present disclosure should not be limited to the particular forms as illustrated, and that all modifications and alterations which maintain the spirit and realm of the present disclosure are within the scope as defined in the appended claims. 

1. An image processing method, comprising: utilizing a processor and a memory to process components of a point A_(i)(r_(i),g_(i),b_(i)) of an original image in RGB color space according to a first function f₁(x) to obtain A₀(r₀,g₀,b₀)=f₁(x_(i)), where i is a natural number; utilizing the processor and the memory to process the point A_(i)(r_(i),g_(i),b_(i)) of the original image according to a second function f₂(x) to obtain a processed saturation S₀=f₂(x_(i)); letting f₂(x_(i))=1 to determine the point A₀(r₀,g₀,b₀), and processing the point A_(i) in the RGB color space for saturation enhancement if max(r₀,g₀,b₀)≤1; converting the point A_(i) into CMY color space for saturation enhancement if max(r₀,g₀,b₀)>1; and displaying an image having the points with saturation enhanced, on a display screen.
 2. The method according to claim 1, wherein the processing step according to the first function comprises: selecting the point A_(i)(r_(i),g_(i),b_(i)) from the original image in the RGB color space; and according to the first function f₁(x), processing each component of the point A_(i) of the original image in the RGB color space to obtain A₀(r₀,g₀,b₀)=f₁(x_(i)), wherein processing each component of the point A_(i) of the original image in the RGB color space is to stretch (α) and translate (β) the components of the point A_(i) in the RGB color space, where the first function is f₁(x)=αx+β.
 3. The method according to claim 1, wherein the processing step according to the second function comprises: selecting the point A_(i)(r_(i),g_(i),b_(i)) from the original image in the RGB color space; according to a third function f₃(x), determining saturation S_(i)=f₃(x_(i)) of the point A_(i)(r_(i),g_(i),b_(i)) of the original image; and according to the second function f₂(x), processing the saturation S_(i) of the original image to obtain the processed saturation S₀=f₂(x_(i)).
 4. The method according to claim 3, wherein the saturation of the point A_(i)(r_(i),g_(i),b_(i)) of the original image is ${S_{i} = {1 - {3 \times \frac{\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}{r_{i} + g_{i} + b_{i}}}}};$ and the processed saturation obtained by processing the saturation S_(i) of the original image according to the second function f₂(x) is $S_{0} = {{f_{2}(x)} = {1 - {3 \times {\frac{{\alpha \cdot {\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}} + \beta}{r_{i} + g_{i} + b_{i}}.}}}}$
 5. The method according to claim 1, wherein brightness of the original image remains unchanged before and after image processing, and values of α and β in f₁(x) are obtained using S₀=f₂(x_(i)) and A₀ (r₀,g₀,b₀)=f₁(x_(i)).
 6. The method according to claim 1, wherein the CMY color space is a color model based on subtractive color mixture, and the point A_(i) is processed in the CMY color space using a fourth function f₄(x)=1−x, where x represents each component of the point A_(i)(r_(i),g_(i),b_(i)) in the RGB color space.
 7. An image processing device, comprising: a processor; and a memory connected with the processor, the memory comprising a plurality of program instructions executable by the processor configured to execute a method, the method comprising: processing components of a point A_(i)(r_(i),g_(i),b_(i)) of an original image in RGB color space according to a first function f₁(x) to obtain A₀(r₀,g₀,b₀)=f₁(x_(i)), where i is a natural number, processing the point A_(i)(r_(i),g_(i),b_(i)) of the original image according to a second function f₂(x) to obtain a processed saturation S₀=f₂(x_(i)), determining the point A₀(r₀,g₀,b₀), processing the point A_(i) in the RGB color space for saturation enhancement if max(r₀,g₀,b₀)≤1, and converting the point A_(i) into CMY color space for saturation enhancement if max(r₀,g₀,b₀)>1.
 8. The device according to claim 7, wherein the method further comprises stretching (α) and translating (β) each component of the point A_(i) of the original image in the RGB color space, where the first function is f₁(x)=αx+β.
 9. The device according to claim 7, wherein the method further comprises determining saturation S_(i)=f₃(x_(i)) of the point A_(i)(r_(i),g_(i),b_(i)) of the original image according to a third function f₃(x), where S_(i)=1−3×min[r_(i),g_(i)b_(i)]/r_(i)+g_(i)+b_(i); and processing the saturation S_(i) of the original image according to the second function f₂(x) to obtain the processed saturation $S_{0} = {{f_{2}(x)} = {1 - {3 \times {\frac{{\alpha \cdot {\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}} + \beta}{r_{i} + g_{i} + b_{i}}.}}}}$
 10. An image processing method, comprising: utilizing a processor and a memory to process components of a point A_(i)(r_(i),g_(i),b_(i)) of an original image in RGB color space according to a first function f₁(x) to obtain A₀(r₀,g₀,b₀)=1,(x_(i)), where i is a natural number; utilizing the processor and the memory to process the point A_(i)(r_(i),g_(i),b_(i)) of the original image according to a second function f₂(x) to obtain a processed saturation S₀=f₂(x_(i)); and letting f₂(x_(i))=1 to determine the point A₀(r₀,g₀,b₀), and processing the point A_(i) in the RGB color space for saturation enhancement if max(r₀,g₀,b₀)≤1; using a fourth function f₄(x) to convert the point A_(i) into CMY color space for saturation enhancement if max(r₀,g₀,b₀)>1; and displaying an image having the points with saturation enhanced, on a display screen.
 11. The method according to claim 10, wherein the processing step according to the first function comprises: selecting the point A_(i)(r_(i),g_(i),b_(i)) from the original image in the RGB color space; and according to the first function f₁(x), processing each component of the point A_(i) of the original image in the RGB color space to obtain A₀(r₀,g₀,b₀)=f₁(x_(i)), wherein processing each component of the point A_(i) of the original image in the RGB color space is to stretch (α) and translate (β) the components of the point A_(i) in the RGB color space, where the first function is f₁(x)=αx+β.
 12. The method according to claim 10, wherein the processing step according to the second function comprises: selecting the point A_(i)(r_(i),g_(i),b_(i)) from the original image in the RGB color space; according to a third function f₃(x), determining saturation S_(i)=f₃(x_(i)) of the point A_(i)(r_(i),g_(i),b_(i)) of the original image; and according to the second function f₂(x), processing the saturation S_(i) of the original image to obtain the processed saturation S₀=f₂(x_(i)).
 13. The method according to claim 12, wherein the saturation of the point A_(i)(r_(i),g_(i),b_(i)) of the original image is ${S_{i} = {1 - {3 \times \frac{\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}{r_{i} + g_{i} + b_{i}}}}};$ and the processed saturation obtained by processing the saturation S_(i) of the original image according to the second function f₂(x) is $S_{0} = {{f_{2}(x)} = {1 - {3 \times {\frac{{\alpha \cdot {\min \left\lbrack {r_{i},g_{i},b_{i}} \right\rbrack}} + \beta}{r_{i} + g_{i} + b_{i}}.}}}}$
 14. The method according to claim 10, wherein brightness of the original image remains unchanged before and after image processing, and values of α and β in f₁(x) are obtained using S₀=f₂(x_(i)) and A₀(r₀,g₀,b₀)=f₁(x_(i)).
 15. The method according to claim 10, wherein the CMY color space is a color model based on subtractive color mixture, and the point A_(i) is processed in the CMY color space using a fourth function f₄(x)=1−x, where x represents each component of the point A_(i)(r_(i),g_(i),b_(i)) in the RGB color space. 